/**
* Copyright (c) 2009, Regents of the University of Colorado
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
* Neither the name of the University of Colorado at Boulder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
package clear.train.kernel;

import java.util.ArrayList;
import java.util.Arrays;

import clear.util.tuple.JIntDoubleTuple;

import com.carrotsearch.hppc.IntArrayList;

/**
 * Abstract kernel.
 * @author Jinho D. Choi
 * <b>Last update:</b> 11/5/2010
 */
abstract public class AbstractKernel
{
	static public final byte KERNEL_NONE       = 0;
	static public final byte KERNEL_POLYNOMIAL = 1;
	static public final byte KERNEL_DAEM       = 2;
	
	/** Delimiter between index and value (e.g. 3:0.12) */
	static public final String FTR_DELIM = ":";
	/** Delimiter between columns (e.g. 0:0.12 3:0.45) */
	static public final String COL_DELIM = " ";
	
	/** Total number of training instances */
	public int N; 
	/** Total number of features */
	public int D;
	/** Total number of labels */
	public int L;
	/** List of labels */
	public int[] a_labels;    
	/** Training labels */
	public IntArrayList        a_ys;
	/** Training feature indices */
	public ArrayList<int[]>    a_xs;
	/** Training feature values */
	public ArrayList<double[]> a_vs;
	/** Kernel type */
	public byte kernel_type;
	/** true if binary features only */
	public boolean b_binary;
		
	public AbstractKernel(byte kernelType)
	{
		kernel_type = kernelType;
		b_binary    = true;
	}
	
	/**
	 * Calls {@link AbstractKernel#init(String)}
	 * @param instanceFile name of a file containing training instances
	 */
	public AbstractKernel(byte kernelType, String instanceFile)
	{
		this(kernelType);
		
		try
		{
			init(instanceFile);
		}
		catch (Exception e) {e.printStackTrace();}
	}
	
	static public int getScala(int[] xi, int[] xj)
	{
		int scala = 0, i;
		
		for (i=0; i<xi.length; i++)
		{
			if (Arrays.binarySearch(xj, xi[i]) >= 0)
				scala++;
		}
		
		return scala;
	}
	
	static public int getScala(IntArrayList xi, int[] xj)
	{
		int scala = 0, i;
		
		for (i=0; i<xi.size(); i++)
		{
			if (Arrays.binarySearch(xj, xi.get(i)) >= 0)
				scala++;
		}
		
		return scala;
	}
	
	static public double getScala(int[] xi, int[] xj, double[] vi, double[] vj)
	{
		double scala = 0;
		int i, j;
		
		for (i=0; i<xi.length; i++)
		{
			if ((j = Arrays.binarySearch(xj, xi[i])) >= 0)
				scala += vi[i] * vj[j];
		}
		
		return scala;
	}
	
	static public double getScala(ArrayList<JIntDoubleTuple> xvi, int[] xj,  double[] vj)
	{
		double scala = 0;
		int j;
		
		for (JIntDoubleTuple tup : xvi)
		{
			if ((j = Arrays.binarySearch(xj, tup.i)) >= 0)
				scala += tup.d * vj[j];
		}
		
		return scala;
	}
	
	/** Normalizes a weight vector. */
	static public void normalize(double[] weight)
	{
		double norm = 0;
		
		for (int i=0; i<weight.length; i++)
			norm += (weight[i] * weight[i]);
		
		norm = Math.sqrt(norm);
		
		for (int i=0; i<weight.length; i++)
			weight[i] /= norm;
	}
	
	/** Kernelizes this feature space  */
	abstract protected void init(String instanceFile) throws Exception;
}
